Data Collection and Management of mHealth, Wearables, and Internet of Things in Digital Behavioral Health Interventions With the Awesome Data Acquisition Method (ADAM): Development of a Novel Informatics Architecture.

Unlabelled: The integration of health and activity data from various wearable devices into research studies presents technical and operational challenges. The Awesome Data Acquisition Method (ADAM) is a versatile, web-based system that was designed for integrating data from various sources and managing a large-scale multiphase research study. As a data collecting system, ADAM allows real-time data collection from wearable devices through the device's application programmable interface and the mobile app's adaptive real-time questionnaires. As a clinical trial management system, ADAM integrates clinical trial management processes and efficiently supports recruitment, screening, randomization, data tracking, data reporting, and data analysis during the entire research study process. We used a behavioral weight-loss intervention study (SMARTER trial) as a test case to evaluate the ADAM system. SMARTER was a randomized controlled trial that screened 1741 participants and enrolled 502 adults. As a result, the ADAM system was efficiently and successfully deployed to organize and manage the SMARTER trial. Moreover, with its versatile integration capability, the ADAM system made the necessary switch to fully remote assessments and tracking that are performed seamlessly and promptly when the COVID-19 pandemic ceased in-person contact. The remote-native features afforded by the ADAM system minimized the effects of the COVID-19 lockdown on the SMARTER trial. The success of SMARTER proved the comprehensiveness and efficiency of the ADAM system. Moreover, ADAM was designed to be generalizable and scalable to fit other studies with minimal editing, redevelopment, and customization. The ADAM system can benefit various behavioral interventions and different populations.

Development and Utility of an Interactive Online Dashboard for Monitoring Data Collection and Data Processing During a School-Based Health Survey.

We built an interactive online dashboard using Google Looker Studio to monitor data collection and data processing activities during the Adolescent Health Survey (AHS) 2022, a large-scale nationwide survey conducted among school-going adolescents in Malaysia. Through user testing and training, refinements were made to the initial dashboard, resulting in a more streamlined and concise dashboard design. The dashboard comprised 2 pages that provided key metrics on the progress of data collection and data processing, respectively. The introduction of the dashboard enhanced the quality and ease of weekly progress reporting during meetings of the survey's central coordinating team, while its drill-down and filtering functionalities helped us detect arising issues early and supported collaborative problem-solving. Research teams coordinating comparable school-based health surveys are invited to duplicate the dashboard using Looker Studio's built-in "Make a copy" function and customize it further based on their country- or survey-specific requirements.

Health education can save the environment from medicine residues

Abstract The incorrect disposal of medicines and their environmental impact has been related to the health medicalization and the improper use of medication by society. In this sense, it is very important to know the profile of drug disposal for foster health policies. The aim was to identify the profile of disposal of medicines by the population, including the cost perspective. This is an inquiry descriptive study that began in September 2019. Medicine disposal health education program was carried out over six months in two University pharmacies. A questionnaire for sociodemographic and discarded medicines data collection was applied. Logistic regression analysis for variables association of correct disposal and the chi-square and t-student analysis for comparison between disposal programs were performed for a level of 5% and test power of 80%. Medicines weighed 23.3 kg and 28.5 kg, with the cost variation from US$ 13.5 to US$ 16.1 until the final treatment. The correct disposal was strongly associated with the disposal reason (p=0.013), source of information (p=0.006), prescription (p=0.03), form of use (p=0.01), acquisition source (p=0.001), cost with medication (p=0.0001), education (p=0.028) and age (p=0.05). The correct medicine disposal was associated with important features of the community related to education health.

Algorithm for wireless sensor networks in ginseng field in precision agriculture.

In the research on energy-efficient networking methods for precision agriculture, a hot topic is the energy issue of sensing nodes for individual wireless sensor networks. The sensing nodes of the wireless sensor network should be enabled to provide better services with limited energy to support wide-range and multi-scenario acquisition and transmission of three-dimensional crop information. Further, the life cycle of the sensing nodes should be maximized under limited energy. The transmission direction and node power consumption are considered, and the forward and high-energy nodes are selected as the preferred cluster heads or data-forwarding nodes. Taking the cropland cultivation of ginseng as the background, we put forward a particle swarm optimization-based networking algorithm for wireless sensor networks with excellent performance. This algorithm can be used for precision agriculture and achieve optimal equipment configuration in a network under limited energy, while ensuring reliable communication in the network. The node scale is configured as 50 to 300 nodes in the range of 500 × 500 m2, and simulated testing is conducted with the LEACH, BCDCP, and ECHERP routing protocols. Compared with the existing LEACH, BCDCP, and ECHERP routing protocols, the proposed networking method can achieve the network lifetime prolongation and mitigate the decreased degree and decreasing trend of the distance between the sensing nodes and center nodes of the sensor network, which results in a longer network life cycle and stronger environment suitability. It is an effective method that improves the sensing node lifetime for a wireless sensor network applied to cropland cultivation of ginseng.

Potential interactions between psychotropic drugs and alcohol and tobacco dependence

Abstract The aim of this study was to identify and analyze the potential interactions between psychotropic drugs and alcohol and tobacco addiction. A cross-sectional study was carried out on secondary data collection in a Center for Psychosocial Care in Alcohol and Other Drugs. Subjects aged 18 years old and over, with alcohol and tobacco dependence, who were taking psychotherapies were included. Medical records with the most recent prescriptions were reviewed. Potential interactions between psychotropic drugs and alcohol and tobacco were analyzed using the Micromedex database and stratified according to clinical risks and mechanisms of action. The Pearson's Chi-square test was used to find significant associations between the variables of interest. The significance level was set at 5%. Between the 2010-2018 period, 2,322 subjects were treated at the care center. Of these, 1,020 fulfilled the inclusion criteria, out of whom 515 (50.5%) were dependent on alcohol and 310 (30.4%) were dependent on tobacco. We found 1,099 potential interactions between psychotropic drugs and alcohol and 160 potential interactions between psychotropic drugs and tobacco. In relation to alcohol dependence, psychotropic drugs interacted largely with moderate clinical risk, and pharmacokinetic mechanisms of action. In relation to tobacco dependence, high clinical risk interactions and pharmacodynamic mechanisms of action predominated.

Sodium alendronate: proposal and reliability of indicators

Abstract The present study proposes and evaluates the test-retest reliability of indicators of the correct use of sodium alendronate in elderly patients. This is a test-retest reliability study for use of sodium alendronate. Six questions to evaluate the correct use of this medicine were elaborated after analysis of information in the literature. Data collection was performed through questionnaires in face-to-face in-home interviews by previously trained interviewers. The participants were initially interviewed (test) when they agreed to participate in the study, and secondly (retest), after a period of 7 to 14 days from the first interview. The reliability of the questions was evaluated by means of the agreement percentage and the Kappa coefficient. Fifty-seven pairs (test-retest) were obtained. The mean age was 69.3 (SD = 6.9) years, the majority (92.5%) completed elementary education, and declared themselves white (50.9%). All the questions presented high concordance ranging from 79.0% to 98.3%. The Kappa values ranged from 0.1 (low) to 0.83 (very good). The agreement percentage and the Kappa values suggest adequate reliability of the proposed questions. We suggest that they can be used as a simple and quick way to evaluate the quality of sodium alendronate use among the elderly.

Machine learning-based real-time object locator/evaluator for cryo-EM data collection.

In cryo-electron microscopy (cryo-EM) data collection, locating a target object is error-prone. Here, we present a machine learning-based approach with a real-time object locator named yoneoLocr using YOLO, a well-known object detection system. Implementation shows its effectiveness in rapidly and precisely locating carbon holes in single particle cryo-EM and in locating crystals and evaluating electron diffraction (ED) patterns in automated cryo-electron crystallography (cryo-EX) data collection. The proposed approach will advance high-throughput and accurate data collection of images and diffraction patterns with minimal human operation.

An automated multiplexed turbidometric and data collection system for measuring growth kinetics of anaerobes dependent on gaseous substrates.

Standard methods of monitoring the growth kinetics of anaerobic microorganisms are generally impractical when there is a protracted or indeterminate period of active growth, and when high numbers of samples or replications are required. As part of our studies of the adaptive evolution of a simple anaerobic syntrophic mutualism, requiring the characterization of many isolates and alternative syntrophic pairings, we developed a multiplexed growth monitoring system using a combination of commercially available electronics and custom designed circuitry and materials. This system automatically monitors up to 64 sealed, and as needed pressurized, culture tubes and reports the growth data in real-time through integration with a customized relational database. The utility of this system was demonstrated by resolving minor differences in growth kinetics associated with the adaptive evolution of a simple microbial community comprised of a sulfate reducing bacterium, Desulfovibrio vulgaris, grown in syntrophic association with Methanococcus maripaludis, a hydrogenotrophic methanogen.

TailTimer: A device for automating data collection in the rodent tail immersion assay.

The tail immersion assay is a widely used method for measuring acute thermal pain in a way which is quantifiable and reproducible. It is non-invasive and measures response to a stimulus that may be encountered by an animal in its natural environment. However, quantification of tail withdrawal latency relies on manual timing of tail flick using a stopwatch, and precise temperatures of the water at the time of measurement are most often not recorded. These two factors greatly reduce the reproducibility of tail immersion assay data and likely contribute to some of the discrepancies present among relevant literature. We designed a device, TailTimer, which uses a Raspberry Pi single-board computer, a digital temperature sensor, and two electrical wires, to automatically record tail withdrawal latency and water temperature. We programmed TailTimer to continuously display and record water temperature and to only permit the assay to be conducted when the water is within ± 0.25°C of the target temperature. Our software also records the identification of the animals using a radio frequency identification (RFID) system. We further adapted the RFID system to recognize several specific keys as user interface commands, allowing TailTimer to be operated via RFID fobs for increased usability. Data recorded using the TailTimer device showed a negative linear relationship between tail withdrawal latency and water temperature when tested between 47-50°C. We also observed a previously unreported, yet profound, effect of water mixing speed on latency. In one experiment using TailTimer, we observed significantly longer latencies following administration of oral oxycodone versus a distilled water control when measured after 15 mins or 1 h, but not after 4 h. TailTimer also detected significant strain differences in baseline latency. These findings valorize TailTimer in its sensitivity and reliability for measuring thermal pain thresholds.

Surgical data recording in the operating room: a systematic review of modalities and metrics.

INTRODUCTION: Operating room recording, via video, audio and sensor-based recordings, is increasingly common. Yet, surgical data science is a new field without clear guidelines. The purpose of this study is to examine existing published studies of surgical recording modalities to determine which are available for use in the operating room, as a first step towards developing unified standards for this field. METHODS: Medline, EMBASE, CENTRAL and PubMed databases were systematically searched for articles describing modalities of data collection in the operating room. Search terms included 'video-audio media', 'bio-sensing techniques', 'sound', 'movement', 'operating rooms' and others. Title, abstract and full-text screening were completed to identify relevant articles. Descriptive statistical analysis was performed for included studies. RESULTS: From 3756 citations, 91 studies met inclusion criteria. These studies described 10 unique data-collection modalities for 17 different purposes in the operating room. Data modalities included video, audio, kinematic and eye-tracking among others. Data-collection purposes described included surgical trainee assessment, surgical error, surgical team communication and operating room efficiency. CONCLUSION: Effective data collection and utilization in the operating room are imperative for the provision of superior surgical care. The future operating room landscape undoubtedly includes multiple modalities of data collection for a plethora of purposes. This review acts as a foundation for employing operating room data in a way that leads to meaningful benefit for patient care.

Graph Regularized Flow Attention Network for Video Animal Counting From Drones.

In this paper, we propose a large-scale video based animal counting dataset collected by drones (AnimalDrone) for agriculture and wildlife protection. The dataset consists of two subsets, i.e., PartA captured on site by drones and PartB collected from the Internet, with rich annotations of more than 4 million objects in 53, 644 frames and corresponding attributes in terms of density, altitude and view. Moreover, we develop a new graph regularized flow attention network (GFAN) to perform density map estimation in dense crowds of video clips with arbitrary crowd density, perspective, and flight altitude. Specifically, our GFAN method leverages optical flow to warp the multi-scale feature maps in sequential frames to exploit the temporal relations, and then combines the enhanced features to predict the density maps. Moreover, we introduce the multi-granularity loss function including pixel-wise density loss and region-wise count loss to enforce the network to concentrate on discriminative features for different scales of objects. Meanwhile, the graph regularizer is imposed on the density maps of multiple consecutive frames to maintain temporal coherency. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method, compared with several state-of-the-art counting algorithms. The AnimalDrone dataset is available at https://github.com/VisDrone/AnimalDrone.

Clinical Trials Without Clinical Sites.

Clinical trials conducted at clinical sites are limited to enrolling people who live nearby and are able to attend visits at clinics. Some types of clinical trials can be performed without clinical sites, which enables people to participate regardless of proximity to a clinical site or limitations that make visits difficult. Trials at clinical sites involve face-to-face relationships with in-person collection of informed consent, examinations, data, and specimens. In contrast, without clinical sites, informed consent and data are obtained online, limited examinations can be performed by telemedicine or visiting research nurses, biospecimens can be collected by visiting nurses or local laboratories, and treatments can be sent to homes or administered by nurses in participants' homes. Trials without clinical sites require internet access and must adapt to the lack of face-to-face interactions with study staff, with communication conducted by email, telephone, or video. Many trials cannot be performed entirely without clinical sites because they require examinations, tests, or treatments that must be given at a clinical site. However, some of the methods required for trials without sites, such as online data collection, follow-up visits by telemedicine or research nurses, and delivery of treatments to home, could reduce the need for visits to clinical sites and reduce the burden of participating in a clinical trial. When feasible, conducting clinical trials without clinical sites has the potential to expand participation and the generalizability of their results.

Smart Glasses as a Surgical Pathology Grossing Tool.

CONTEXT.­: Smart glasses are a wearable technology that enable hands-free data acquisition and entry. OBJECTIVE.­: To develop a surgical pathology grossing application on a smart glass platform. DESIGN.­: An existing logistics software for the Google Glass Enterprise smart glass platform was used to create surgical pathology grossing protocols. The 2 grossing protocols were developed to simulate grossing a complex (heart) and a simple (kidney) specimen. For both protocols, users were visually prompted by the smart glass device to perform each task, record measurements, or document the field of view. In addition to measuring the total time of the protocol performance, each substep within the protocol was automatically recorded. Subsequently, a report was generated that contained the dictation, images, voice recordings, and the timing of each step. The application was tested by 3 users using the 2 grossing protocols. The users were tracked across 3 grossing procedures for each protocol. RESULTS.­: For the complex specimen grossing the average time across repeated procedures was not significantly different between users (P > .99). However, when grossing times of the complex specimen were compared for repeated performances of the same user, a significant reduction in grossing times was observed with each repetition (P = .002). For the simple specimen, the average grossing time across multiple attempts was different among users (P = .03); however, no improvement in grossing time was observed with repeated performance (P = .499). CONCLUSIONS.­: Augmented reality based grossing applications can provide automated data collection to track the changes in grossing performance over time.

Fit-for-Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience.

Biometric monitoring technologies (BioMeTs) are becoming increasingly common to aid data collection in clinical trials and practice. The state of BioMeTs, and associated digitally measured biomarkers, is highly reminiscent of the field of laboratory biomarkers 2 decades ago. In this review, we have summarized and leveraged historical perspectives, and lessons learned from laboratory biomarkers as they apply to BioMeTs. Both categories share common features, including goals and roles in biomedical research, definitions, and many elements of the biomarker qualification framework. They can also be classified based on the underlying technology, each with distinct features and performance characteristics, which require bench and human experimentation testing phases. In contrast to laboratory biomarkers, digitally measured biomarkers require prospective data collection for purposes of analytical validation in human subjects, lack well-established and widely accepted performance characteristics, require human factor testing, and, for many applications, access to raw (sample-level) data. Novel methods to handle large volumes of data, as well as security and data rights requirements add to the complexity of this emerging field. Our review highlights the need for a common framework with appropriate vocabulary and standardized approaches to evaluate digitally measured biomarkers, including defining performance characteristics and acceptance criteria. Additionally, the need for human factor testing drives early patient engagement during technology development. Finally, use of BioMeTs requires a relatively high degree of technology literacy among both study participants and healthcare professionals. Transparency of data generation and the need for novel analytical and statistical tools creates opportunities for precompetitive collaborations.

The Development of Data Collection Tools to Measure Parent-Infant Closeness and Family-Centered Care in NICUs.

BACKGROUND: Preterm and sick infants benefit from parent-infant closeness and family-centered care (FCC) in neonatal intensive care units (NICUs). Prospective and feasible tools are needed to measure these care practices to facilitate their implementation. AIMS: To describe the development process of three prospective data collection tools that measure parent-infant closeness and the quality of FCC. METHODS: Data collection tools were developed in an iterative process consisting of three development cycles. Feedback was gathered from parents, staff, and researchers. The first stages of development focused on the content validity, appropriate scaling, and optimization of the response rate of these tools. RESULTS: The study included parents of 490 infants and the nurses working at bedside in 15 NICUs in six countries. The Parent-Infant Closeness Diary was developed to measure the daily duration of parental presence, holding, and skin-to-skin contact. The optimal duration for daily diaries was 14 consecutive days to maintain a good response rate. Parents provided reliable documentation of parent-infant closeness. Digital FCC tools covering the nine aspects of FCC for parents and nurses were developed to measure the quality of FCC. Participants provided answers on a 7-point Likert scale. Parents' response rates remained >50% for approximately 1 month, and the nurses' mean response rate was 55% (39%-87%) for the 3-month study period. LINKING EVIDENCE TO ACTION: These new tools provide prospective daily information to aid the implementation of parent-infant closeness and the quality of FCC in NICU in different countries. They can be used to study and evaluate the implementation of these clinical practices NICUs in an international context.

A self-powered analog sensor-data-logging device based on Fowler-Nordheim dynamical systems.

Continuous, battery-free operation of sensor nodes requires ultra-low-power sensing and data-logging techniques. Here we report that by directly coupling a sensor/transducer signal into globally asymptotically stable monotonic dynamical systems based on Fowler-Nordheim quantum tunneling, one can achieve self-powered sensing at an energy budget that is currently unachievable using conventional energy harvesting methods. The proposed device uses a differential architecture to compensate for environmental variations and the device can retain sensed information for durations ranging from hours to days. With a theoretical operating energy budget less than 10 attojoules, we demonstrate that when integrated with a miniature piezoelectric transducer the proposed sensor-data-logger can measure cumulative "action" due to ambient mechanical acceleration without any additional external power.

Methods for Cleaning and Managing a Nurse-Led Registry.

BACKGROUND: Clinical registries provide insight on the quality of patient care by providing data to identify associations and patterns in diagnosis, disease, and treatment. This has led to a push toward using large data sets in healthcare research. Nurse researchers are developing data registries, but most are unaware of how to manage a data registry. This article examines a neuroscience nursing registry to describe a quality control and data management process. DATA QUALITY PROCESS: Our registry contains more than 90 000 rows of data from almost 5000 patients at 4 US hospitals. Data management is a continuous process that consists of 5 phases: screening, data organization, diagnostic, treatment, and missing data. These phases are repeated with each registry update. DISCUSSION: The interdisciplinary approach to data management resulted in high-quality data, which was confirmed by missing data analysis. Most technical errors could be systematically diagnosed and resolved using basic statistical outputs, and fixed in the source file. CONCLUSION: The methods described provide a structured way for nurses and their collaborators to clean and manage registries.

Age-Dependent and Seasonal Changes in Menstrual Cycle Length and Body Temperature Based on Big Data.

OBJECTIVE: To evaluate the effects of age and season on menstrual cycle length and basal body temperature (BBT). We also examined the effects of climate on cycle length and BBT, taking into account Japanese geographic and social characteristics. METHODS: In this retrospective cohort study, we analyzed data from 6 million menstrual cycles entered into a smartphone application from 310,000 females from 2016 to 2017. Only those who entered more than 10 cycles in 2 years were included. Generalized estimation equations were used to adjust for confounding factors and for within-person correlations of multiple records. Multiple regression analysis was conducted, with age, external average temperature, precipitation amount, and sunshine hours as confounding factors. RESULTS: The mean menstrual cycle length increased from age 15-23 years, subsequently decreased up to age 45 years, and then increased again. Average follicular phase body temperature showed no significant age-dependent changes, but luteal phase body temperature gradually increased up to 29 years and then stabilized and started to decrease after age 42 years. A significant association between external temperature and body temperature (follicular and luteal phase) was observed, though menstrual cycle length did not show such an association. CONCLUSION: These results, derived from data self-entered into a smartphone application, revealed underrecognized age-dependent and seasonal changes in menstrual cycle length and BBT, which will contribute to a better understanding of female reproductive health in the modern world.